class_docstr: Adjiman objective function.
  
  The Adjiman [1]_ global optimization problem is a multimodal minimization
  problem defined as follows:
  
  .. math::
  
      f_{\text{Adjiman}}(x) = \cos(x_1)\sin(x_2) - \frac{x_1}{(x_2^2 + 1)}
  
  
  with, :math:`x_1 \in [-1, 2]` and :math:`x_2 \in [-1, 1]`.
  
  *Global optimum*: :math:`f(x) = -2.02181` for :math:`x = [2.0, 0.10578]`
  
  .. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
  For Global Optimization Problems Int. Journal of Mathematical Modelling
  and Numerical Optimisation, 2013, 4, 150-194.
code: def __init__(self, dimensions=2):
      Benchmark.__init__(self, dimensions)
  
      self._bounds = ([-1.0, 2.0], [-1.0, 1.0])
      self.global_optimum = [[2.0, 0.10578]]
      self.fglob = -2.02180678
  
imports: from numpy import abs, cos, exp, pi, prod, sin, sqrt, sum
  from .go_benchmark import Benchmark
label_desc: Use image node_id 1 to create a new Adjiman object from inherited base classes: Benchmark with example: obj = Adjiman(dimensions)